| End-to-end text-dependent speaker verification G Heigold, I Moreno, S Bengio, N Shazeer 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 463 | 2016 |
| Small-footprint keyword spotting using deep neural networks G Chen, C Parada, G Heigold 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 358 | 2014 |
| Multilingual acoustic models using distributed deep neural networks G Heigold, V Vanhoucke, A Senior, P Nguyen, MA Ranzato, M Devin, ... 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 290 | 2013 |
| Sequence discriminative distributed training of long short-term memory recurrent neural networks H Sak, O Vinyals, G Heigold, A Senior, E McDermott, R Monga, M Mao | 144 | 2014 |
| Word embeddings for speech recognition S Bengio, G Heigold | 143 | 2014 |
| The RWTH Aachen University open source speech recognition system D Rybach, C Gollan, G Heigold, B Hoffmeister, J Lööf, R Schlüter, H Ney Tenth Annual Conference of the International Speech Communication Association, 2009 | 128 | 2009 |
| An empirical study of learning rates in deep neural networks for speech recognition A Senior, G Heigold, M Ranzato, K Yang 2013 IEEE international conference on acoustics, speech and signal …, 2013 | 126 | 2013 |
| The RWTH 2007 TC-STAR evaluation system for European English and Spanish J Lööf, C Gollan, S Hahn, G Heigold, B Hoffmeister, C Plahl, D Rybach, ... Eighth Annual Conference of the International Speech Communication Association, 2007 | 77 | 2007 |
| Asynchronous stochastic optimization for sequence training of deep neural networks G Heigold, E McDermott, V Vanhoucke, A Senior, M Bacchiani 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 67 | 2014 |
| An image is worth 16x16 words: Transformers for image recognition at scale A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... arXiv preprint arXiv:2010.11929, 2020 | 65 | 2020 |
| Modified MMI/MPE: A direct evaluation of the margin in speech recognition G Heigold, T Deselaers, R Schlüter, H Ney Proceedings of the 25th international Conference on Machine Learning, 384-391, 2008 | 64 | 2008 |
| Multiframe deep neural networks for acoustic modeling V Vanhoucke, M Devin, G Heigold 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 53 | 2013 |
| A linguistic evaluation of rule-based, phrase-based, and neural MT engines A Burchardt, V Macketanz, J Dehdari, G Heigold, JT Peter, P Williams The Prague Bulletin of Mathematical Linguistics 108 (1), 159-170, 2017 | 51 | 2017 |
| Discriminative training for automatic speech recognition: Modeling, criteria, optimization, implementation, and performance G Heigold, H Ney, R Schluter, S Wiesler IEEE Signal Processing Magazine 29 (6), 58-69, 2012 | 49 | 2012 |
| Equivalence of generative and log-linear models G Heigold, H Ney, P Lehnen, T Gass, R Schluter IEEE Transactions on Audio, Speech, and Language Processing 19 (5), 1138-1148, 2010 | 49 | 2010 |
| Cross-lingual, Character-level neural morphological tagging R Cotterell, G Heigold arXiv preprint arXiv:1708.09157, 2017 | 43 | 2017 |
| The 2006 RWTH parliamentary speeches transcription system J Lööf, M Bisani, C Gollan, G Heigold, B Hoffmeister, C Plahl, R Schlüter, ... Ninth International Conference on Spoken Language Processing, 2006 | 42 | 2006 |
| Object classification by fusing SVMs and Gaussian mixtures T Deselaers, G Heigold, H Ney Pattern Recognition 43 (7), 2476-2484, 2010 | 41 | 2010 |
| A log-linear discriminative modeling framework for speech recognition G Heigold | 39 | 2010 |
| A Gaussian mixture model layer jointly optimized with discriminative features within a deep neural network architecture E Variani, E McDermott, G Heigold 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 38 | 2015 |
